Online advertising has seen phenomenal growth over the last few years, growing hand-in-hand with the expansion of the Internet. It has evolved from randomly displayed, passive advertisements to advertisements targeted to specific individuals based on their demographic and psychographic profile. Examples of such technologies are given by U.S. Pat. No. 7,062,466, U.S. Pat. No. 7,007,074 and US Patent Applications 20050216335, 20070038500, 20080243480, 20080228568 and 20090070219. What all these approaches to advertising hold in common is establishing the appropriate context for the advertisement. This context is established by matching a specific individual or consumer group with the media or content that provides the context for the advertisement. Broadly, contextualizing is known as relevance matching. It is apparent that online advertising is struggling to resolve the problem of matching the message of the advertisement with the context of media placements such as web pages.
Relevance matching, however, is only one small portion of a much larger advertising process. The typical advertising process is complex and time-intensive. As a result, it excludes many small businesses and individuals who lack the professional expertise. Some of the essential elements of the typical advertising process comprise:                recognizing an audience by identifying prospects of unmet needs and evaluating the environment within which the audience exists;        evaluating and identifying market segments;        developing a strategy to target audiences by positioning and developing messaging as well as choosing appropriate media placement and buys;        developing and managing an advertisement campaign;        developing and executing sales-related feedback;        performing analysis on sales-related data; and        making necessary corrections to the whole process in light of campaign performance.        
This cumbersome situation has only become more complex with the advent of online advertising and the evolution to context-generated advertisements. These approaches represent technical improvements, but are still rooted within the conventional process; the advertiser must still perform the demanding tasks of market analysis, segmentation, messaging, and campaign management. Other companies are already using keywords and semantic technology to improve the matching of existing advertiser-created messages with target segments, but the segmentation must be determined in advance by the advertiser. Analyzing a web page for its meaning allows them to better determine whether to target it with a given message, but the targeting is based on many difficult and expensive decisions on the part of the advertiser. Again, these examples represent technical improvements within the existing conventional process for advertising.
There is presently no technique directed to the over-riding process for advertising, to enable the discovery of the optimal market segmentation and messaging for promoted content; to identify and generate relevant relationships and messaging between an advertiser's content and an individual consumer that can be beneficially utilized by a consumer in response to a consumer action.